Protein-based prognostic signature for predicting the survival and immunotherapeutic efficiency of endometrial carcinoma.

ABSTRACT:

Background

Endometrial cancer (EC) is the most frequent malignancy of the female genital tract worldwide. Our study aimed to construct an effective protein prognostic signature to predict prognosis and immunotherapy responsiveness in patients with endometrial carcinoma.

Methods

Protein expression data, RNA expression profile data and mutation data were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA). Prognosis-related proteins in EC patients were screened by univariate Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis were performed to establish the protein-based prognostic signature. The CIBERSORT algorithm was used to quantify the proportions of immune cells i

SUBMITTER: Lai J 

PROVIDER: S-EPMC8957185 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature